May 16, 2020

Industry 4.0's outsized impact on the back office

Industry 4.0
Fourth Industrial Revolution
Oliver Wyman
white collar workers
6 min
Industry 4.0's outsized impact on the back office
The fourth industrial revolution will digitally transform manufacturing, but this time, white-collar staff could feel much of the impact—and reap...

The fourth industrial revolution will digitally transform manufacturing, but this time, white-collar staff could feel much of the impact—and reap potential new benefits.


Most of the buzz generated by digital manufacturing breakthroughs such as 3D printing, big data analytics and human-robot collaboration (HRC) focuses on the impact expected on the factory floor. While plant operations and workers will definitely experience disruptions due to Industry 4.0[1], this industrial revolution may actually have its most profound effects in the offices and bullpens of white-collar staff.  

Industry 4.0 integrates digital connectivity with manufacturing technology to build products faster, better and cheaper. By 2030, the global value-added potential of this wave of innovation will reach 1.4 trillion US dollars, based on research conducted by Oliver Wyman that covers a variety of manufacturing industries.[2] By providing real-time information about customer demand, production capacity, operational performance and product quality, Industry 4.0 will enable decision making that dramatically improves process efficiency in everything from pricing to production planning. Consequently, while the factory floor will undoubtedly benefit enormously from Industry 4.0, the greatest gains could actually occur in non-production areas. And as Industry 4.0 digital innovations such as algorithm-based decision-making take hold in areas such as R&D, product launches, pricing, planning, dispatching and purchasing, companies will likely automate many jobs currently done by humans. At the same time, however, new digitally supported opportunities will emerge for white-collar employees that possess the required skills and capabilities.

A cross-functional efficiency revolution

The changes will be both broad and deep, with more of the tedious and tough duties taken over by office automation and artificial intelligence capabilities. The following examples illustrate some of the changes ahead for non-manufacturing functions.

  • Demand forecasting and intelligent pricing: Leaders rely on demand forecasts to make important business decisions in everything from sales and production planning to pricing. Today, these forecasts result from estimates made by sales and marketing executives, market expectations, and overall market competition. Imperfect and unresponsive to developments beyond the sales and marketing departments knowledge base, such forecasts typically lack rigor. Instead, some companies have begun to improve on this approach by adding big data techniques. Automatically drawing on data from a wide range of sources, centralized analytic algorithms predict future demand from different customer segments and geographies. Ultimately, real-time, algorithm-based demand forecasting will feed into many related processes, such as market research, sales planning, production planning and scheduling with very little human assistance.
  • Smart purchasing and outsourcing: Benefits will result from increased integration transparency with suppliers, access to a wider range of suppliers and greater flexibility in make-or-buy decisions. Manufacturers will standardize the exchange of fully digitized product- and production-related data with suppliers. Doing so will enable them to overcome a variety of current shortcomings such as inconsistent digitization, which can hamper the request-for-proposal process.
  • R&D efficiency and product launch: Gains will emerge from extensive simulation, data integration, big data pattern recognition and real-time feedback loops. Digital innovations will make R&D more efficient, for example, through the structured analysis of operating data and concurrent mechatronic engineering between manufacturers and suppliers using digital models. As simulations improve, R&D departments will require fewer costly, labor-intensive physical prototypes, replacing them with digital modeling and virtual testing environments.

New challenges, but new opportunities, too

In each of the above examples, Industry 4.0 will drive new levels of efficiency across a manufacturer’s white-collar functions, enabling companies to do much more, often with fewer back office employees. At the same time, however, these digital solutions will generate new business opportunities by, for example, enabling manufacturers to help their business-to-business (B2B) customers reduce costs, improve their own customer offerings and create both value and jobs. Several examples of this phenomenon have already emerged:

  • A leading tier-1 automotive supplier that has focused strongly on Industry 4.0 technologies is tendering its expertise in this area to other manufacturers. The company is offering connectivity solution software and hardware for industrial manufacturing. The primary thrust of the business involves providing greater productivity and improved energy efficiency.[3]
  • A railcar OEM offers a cloud-based software suite that improves asset management and operating efficiency for rail operators. Customers benefit from reduced maintenance and energy costs as well as reduced track usage. By increasing asset utilization, rail operators can reduce their capital expenditures over the long run.
  • A maker of paper manufacturing machinery offers customers software that optimizes input consumption and increases paper production yields. The solution collects and analyses data, makes real-timer estimates of the optimization potential of a process, and automatically adjust process parameters. The OEM charges a monthly fixed fee for installation and can receive success-based revenue streams if the customer achieves agreed-upon savings on production costs. Additionally, the company has the option to build a production consulting business using insights from the data and algorithms.
  • Aircraft component suppliers and OEMs can create new sources of recurring revenue by offering innovative data-driven maintenance, repair and overhaul (MRO) solutions to airline customers. Today, a large aircraft generates over 500 gigabytes of data from 600,000 parameters per flight. Advances in MRO will come from better analysis of this data for predictive maintenance activities. By tracking aircraft health parameters in real-time during operation, companies can formulate efficient MRO plans before equipment reaches critical operating thresholds.

Profiling tomorrow’s white-collar worker

Disruptive yes, but these changes can also create opportunities for the firms and white-collar workers that embrace them. Doing so will require flexibility and a willingness to gain digital savvy, compelling employees to learn different ways of working and to take on new responsibilities. While clerical and administrative white-collar positions are vulnerable to Industry 4.0 disruptions, with the right training and experience, people in those jobs can begin to transition to roles that rely on human problem-solving capabilities and creativity—human talents that algorithms can’t as yet match. Professional staff such as engineers, accountants, lawyers and scientists could also face competition from disruptions like virtual simulations, automated cost controls and advanced decision-making algorithms. But here again, while office automation handles the rote elements of these jobs, companies will aggressively exploit their evolving digital capabilities to create new business models and revenue streams—opportunities that will require skilled human participation to succeed. The connected nature of Industry 4.0 will also allow people with sought-after skills to serve companies on a global basis without leaving home or to participate in crowd-sourcing initiatives, thus significantly expanding their marketability. 

As the above suggests, tomorrow’s white-collar worker will require a different set of skills to succeed. Just as blue-collar workers of the future will need better training and education, focusing particularly on rudimentary science, technology, engineering and mathematics (STEM) disciplines, white-collar workers will also need their own refresher courses. In addition to STEM training, other disciplines include a focus on big data analytics, cybernetics, and data mining and management. Companies themselves will likely focus on innovations like crowdsourcing and create vetted global networks of experts to resolve on their toughest problems. At the same time, the massively expanded attack surfaces created by Industry 4.0 networks mean companies will have to approach digital security from a much more integrated perspective, with business staff taking responsibility for safeguarding data rather than depending solely on the security team.

Industry 4.0 promises to reinvigorate manufacturing, but as with every other industrial revolution, it will also displace workers that lack the skills and training needed to operate in the new environment. There is a bright side, however: the forces behind Industry 4.0 will also open new opportunities for white-collar workers who can adapt to their new connected environment.

Michelle Hill is Vice President, Automotive, at Oliver Wyman


Follow @ManufacturingGL and @NellWalkerMG

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May 11, 2021

5 Minutes With PwC on AI and Big Data in Manufacturing

Georgia Wilson
6 min
PwC | Smart Manufacturing | Artificial Intelligence (AI) | Big Data | Analytics | Technology | Digital Factory | Connected Factory | Digital Transfromation
Manufacturing Global speaks to Kaveh Vessali, PwC Middle East Partner (Digital, Data & AI) on the application of AI and Big Data in Manufacturing

Please could you define what artificial intelligence is, and what Big Data is?

AI is the ability of a machine to perceive its environment and perform tasks that normally require human intelligence, and it’s a whole field of different technologies, techniques and applications. 

Big data is a set of tools and capabilities for working with, for processing, extremely large sets of data. 

How does AI and Big Data work together?

Big data is just one of the enablers of AI, though as we see increasing volumes of data, it’s one of the most important 

How can this be applied to a manufacturing setting?

Broadly speaking, there are many benefits of AI, and the use of data, which include reducing costs, minimising human error, and increasing productivity and efficiency. The important thing to consider is any setting - for the use of any technology - is what is the problem you are trying to solve? Be it merely automating repetitive tasks or to reinventing the nature of work in factories by having humans and machines collaborate in order to make better and faster decisions.  

Why should manufacturers use AI and Big Data when adopting smart manufacturing capabilities, what is the value for manufacturers?

One view is, again, the economic benefits of AI, which come in manufacturing as a result of: 

1. Productivity gains from automating processes and augmenting the work of existing labour forces with various applications of AI technologies. 

2. Increased consumer demand due to the increased ability to personalise and tailor manufactured products, along with higher-quality digital and AI-enhanced products and services. 

Manufacturing (and construction industries) are by nature capital intensive, and in our 2018 report, “The potential impact of AI in the Middle East,” we estimated that the adoption of AI applications could increase the sectors’ contribution to GDP gains by more than 12.4% by 2030. 

How can AI and Big Data help manufacturers to evolve in the Industry 4.0 revolution? What about those already looking at Industry 5.0?

It’s really about the investment you make now, in order to futureproof your business. 

We typically see two broad strategies or approaches to the adoption of AI. There are things that we can do immediately, without any recourse to Big Data - which is to adopt technologies we describe as Sensing, those involving computer vision, for example. There are plenty of use cases where these can be used immediately in manufacturing, such as for automatic fault detection. However, there is a longer term play which requires investing in data - getting the right collection mechanisms in place, storage, data governance, Big Data capabilities etc - in order to develop increasingly valuable machine learning driven AI use cases. This is absolutely necessary for long term adoption success. 

What is the best strategy for organisations looking to realise the value of AI and Big Data in manufacturing?  

AI and Big Data are only one part of a successful smart factory. The organisations that lead on AI adoption are those who have already made the most progress in digitising core business processes. In order get ahead in using AI solutions at scale, there are a number of technology investments and organisational decisions to be made, including: 

1. Digitising processes ultimately leads to improved ability to generate data, and in the manufacturing setting - with many 100s of sensors generating 1000s of measurements in real time, the result is Big Data. Data is key to building AI so reliable and accurate data acquisition, management and governance are key. The production line and factories play a critical and direct role in the data-acquisition process. 

2. AI strategy, both long and short term, begins with the use cases, the business applications. Manufacturers need to ask where they want to use AI and gather these use cases together and prioritising projects based on a balance of expected impact and complexity of implementation. 

Of course, in addition to technology and business processes, people are at the heart of any successful technology adoption. AI teams need to be composed not only of data scientists, also data engineers and solution architects to enable their work, data stewards to ensure accuracy, and increasingly so call “Analytics/AI translators” who are able to communicate with business leaders and technology experts. Culture is also key, and manufacturers need to enable a data and AI-driven culture, building trust in data and algorithms by educating their workforce about AI and its capabilities, how best to extract value. It’s not just the positive of course, but also the risks and limitations, as these when encountered without expectations having been set, can significantly impact willingness to invest. 

What are the challenges when it comes to adopting AI and Big Data in manufacturing?

PwC research has shown that one of the major challenges to implementing AI is uncertainty around return on investment (ROI). As I said, there is significant investment required for a long term data and AI strategy to be successful, and expectations around the time to see tangible returns must be set realistically. 

Many companies also struggle with the data side: collecting and supplying the data that an AI system needs to operate, and ensuring that it is accurate. Again, this speaks to the bigger investments required in digitisation. 

Some of the main challenges for manufacturing companies with implementing AI at a scale from our research include:  

  • 40% → Technologies not mature  
  • 40% → Workforce lacks skills to implement and manage AI  
  • 36% → Uncertain of return on investment  
  • 33% → Data is not mature yet 
  • 32% → lack of transparency and trust  
  • 24% → Work councils and labour unions  
  • 22% → Regulatory hurdles in home & important markets  

One element highlighted here, particularly around lack of trust, and labour unions, is that AI is typically misrepresented in the media as “replacing” workers, and taking jobs. Yes, there are efficiency gains to be made from automation, as there have been since the first industrial revolution. But we believe that Data and AI are at their most valuable when they are used to augment workers, enhancing their abilities and the products being manufactured. 

Another challenge we’re starting to see emerge is cyberattacks increasingly targeting interconnected equipment and machinery in smart factories. PwC recently hosted a webcast, in cooperation with the National Association of Manufacturers in the US and Microsoft to discuss this. 

What are the current trends in AI and Big Data in manufacturing?  

  • We see companies putting slightly more focus on adding AI solutions to core production processes such as the engineering, and assembly and quality testing 
  • Safety is of significant importance, with techniques adopted in protocol adherence capabilities (for example maintaining safe distance from specific machinery) being adopted in more every day scenarios for COVID-19 protocol adherence 
  • There is considerable interest in predictive maintenance for large machinery involved in manufacturing processes, and also supply-chain optimisation

What do you see happening in the AI and Big Data industry in manufacturing in the next 12-18 months? 

Honestly, I think we’ll see a continuance of where we’ve already been going for the last 12- 18 months. AI and data are already being used in manufacturing but this use doesn’t get as much attention in the media as, say, healthcare, but the success stories are there, and they will continue as operations continue their digital journeys. 

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